One of the problems of clinical cancer research is that cancer is not a single disease, but a variety of different diseases that can evolve over time, requiring specific treatments depending on the characteristics of the tumor itself and the patient1. Consequently, the challenge is to move toward patient-oriented cancer research, in order to identify new personalized strategies for the early prediction of cancer treatment outcomes2. This is particularly relevant for pancreatic ductal adenocarcinoma (PDAC), since it is considered a hard-to-treat cancer, with a 5-year survival rate of 11%3.
The late diagnosis, rapid progression, and lack of effective therapies remain the most pressing clinical problems of PDAC. The main challenge is, therefore, to model the patient and identify biomarkers that can be applied in the clinic to select the most effective therapy in line with personalized medicine4,5,6. Over time, novel approaches have been proposed to model cancer diseases: patient-derived organoids (PDOs) and mouse patient-derived xenografts (mPDXs) originated from a source of human tumor tissue. They have been used to reproduce the disease to study the response and the resistance to therapy, as well as disease recurrence7,8,9.
Similarly, interest in zebrafish-based patient-derived xenograft (zPDX) models has increased, thanks to their unique and promising characteristics10, representing a quick and low-cost tool for cancer research11,12. zPDX models require only a small tumor sample size, which makes high-throughput screening of chemotherapy feasible13. The most common technique used for zPDX models is based on complete sample digestion and implantation of the primary cell populations, which partially reproduces the tumor, but has the disadvantages of a lack of tumor microenvironment and crosstalk between malignant and healthy cells14.
This work shows how zPDXs can be used as a preclinical model to identify the chemosensitivity profile of pancreatic cancer patients. The valuable strategy facilitates the xenograft process, since there is no need for cell expansion, allowing for the acceleration of the chemotherapy screening. The strength of the model is that all the microenvironment components are maintained as they are in the patient cancer tissue, because, as it is well known, the behavior of the tumor depends on their interplay15,16. This is highly favorable over alternative methods in the literature, as it is possible to preserve the tumor heterogeneity and contribute to improving the predictability of the treatment outcome and relapse in a patient-specific manner, thus enabling the zPDX model to be used in co-clinical trials. This manuscript describes the steps involved in making the zPDX model, starting with a piece of patient tumor resection and treating it to analyze the response to chemotherapy.